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Compliant and soft hands have gained a lot of attention in the past decade because of their ability to adapt to the shape of the objects, increasing their effectiveness for grasping. However, when it comes to grasping highly flexible objects such as textiles, we face the dual problem: it is the object that will adapt to the shape of the hand or gripper. In this context, the classic grasp analysis or grasping taxonomies are not suitable for describing textile objects grasps. This work proposes a novel definition of textile object grasps that abstracts from the robotic embodiment or hand shape and recovers concepts from the early neuroscience literature on hand prehension skills. This framework enables us to identify what grasps have been used in literature until now to perform robotic cloth manipulation, and allows for a precise definition of all the tasks that have been tackled in terms of manipulation primitives based on regrasps. In addition, we also review what grippers have been used. Our analysis shows how the vast majority of cloth manipulations have relied only on one type of grasp, and at the same time we identify several tasks that need more variety of grasp types to be executed successfully. Our framework is generic, provides a classification of cloth manipulation primitives and can inspire gripper design and benchmark construction for cloth manipulation. ; The research leading to these results receives funding from the European Research Council (ERC) from the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE: CLOTH manIpulation Learning from DEmonstrations) and is also supported by the Spanish State Research Agency through the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656) and the "Ramon y Cajal" Fellowship RYC-2017-22703. ; Peer reviewed

Cloth manipulation has been mostly advancing in perception and modeling methods for cloth state estimation and grasping point detection. In comparison, less attention has been put on end-effector design. Indeed, most implementations use a parallel gripper that can only perform pinch grasps. Instead, a more versatile set of possible grasp types could ease many tasks by providing more support to certain parts of the object, as well as make feasible tasks that become very complex when executed with only pinch grasps. We present a versatile gripper design which, besides the common open-close thumb+finger(s) feature, it has a couple of reconfiguration degrees of freedom that offer, first, a wide base plane to provide support and, second, variable friction surface on the thumb tip. Our gripper can execute a versatile set of grasps that ease some complex tasks such as pick and place folded clothes or fold in the air. In addition, the variable friction mechanism enables a more robust pinch-and-slide manipulation to trace cloth edges. Our evaluation shows the gripper potential to execute a wide variety of cloth manipulation tasks ; This work receives funding from the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE), Spanish State Research Agency through the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656) and the "Ramon y Cajal" Fellowship RYC-2017- 22703 ; Peer reviewed

Learning from Demonstration (LfD) is a paradigm that allows robots to learn complex manipulation tasks that can not be easily scripted, but can be demonstrated by a human teacher. One of the challenges of LfD is to enable robots to acquire skills that can be adapted to different scenarios. In this letter, we propose to achieve this by exploiting the variations in the demonstrations to retrieve an adaptive and robust policy, using Gaussian Process (GP) models. Adaptability is enhanced by incorporating task parameters into the model, which encode different specifications within the same task. With our formulation, these parameters can be either real, integer, or categorical. Furthermore, we propose a GP design that exploits the structure of replications, i.e., repeated demonstrations with identical conditions within data. Our method significantly reduces the computational cost of model fitting in complex tasks, where replications are essential to obtain a robust model. We illustrate our approach through several experiments on a handwritten letter demonstration dataset. ; This work has been partially funded by the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE) and by the Spanish State Research Agency through the Maria de Maeztu Seal of Excellence to IRI [MDM-2016-0656].

Cloth manipulation is a challenging task that, despite its importance, has received relatively little attention compared to rigid object manipulation. In this paper, we provide three benchmarks for evaluation and comparison of different approaches towards three basic tasks in cloth manipulation: spreading a tablecloth over a table, folding a towel, and dressing. The tasks can be executed on any bimanual robotic platform and the objects involved in the tasks are standardized and easy to acquire. We provide several complexity levels for each task, and describe the quality measures to evaluate task execution. Furthermore, we provide baseline solutions for all the tasks and evaluate them according to the proposed metrics. ; This work receives funding from the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE), Spanish State Research Agency through the BURG project (CHIST-ERA - PCIN2019- 103447) and the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016- 0656), the "Ramon y Cajal" Fellowship RYC-2017-22703, the Knut and Alice Wallenberg Foundation, and the Swedish Research Council. ; Peer reviewed